Advances in next-generation sequencing technologies have enabled faster and cheaper generation of whole genome sequencing data, and opened new possibilities for the use of whole genome data for research, clinical, and personalized medicine applications. With a greater number of genomes being sequenced, new challenges have arisen in the storage and analysis of whole genome data, which can comprise several terabytes (TB). It has been estimated that the cost of storing genomic sequence data will soon outstrip the cost of sequencing the genome. The size of these large datasets presents challenges in the physical storage of data, the memory, power, and time requirements for processing and analyzing the data, and the transfer of data to, for instance, a cloud server.
In order to unlock the full potential of whole genome sequencing, there is a need for technologies that make the storage, processing, and analysis of data more manageable and cost-effective.